With the advent of networked electronic media (Internet, Web, mobile phones) “word-of-mouth”—the spontaneous passing of messages between individuals—has reached a new dimension, both quantitatively, as it is much easier to pass information along, and qualitatively since the integrity of such information is better preserved.
This opportunity has not gone unnoticed to the marketing profession which is making extensive usage of it through techniques generically labeled as “viral marketing”. An example of those are the buttons to send pages to friends almost universally present in web sites nowadays. Similarly, e-mail campaigns inviting people to forward the message to friends or product or service where its mere acceptance involves notifying others (such as the free Hotmail promotion) are widespread. Thus, the topic has reached the level of subject matter at business schools. For example, see the article entitled “The Buzz on Buzz,” published in the Harvard Business Review, November-December, 2000.
The article describes how word of mouth has become a key component of many marketing campaigns and gives categorized examples of its usage by various companies although it does not provide any analytical method for implementing or measuring it in a systematic way.
Word of mouth effectiveness is rooted on the size and structure of the participating individuals' social network. Social Network Analysis and information propagation dynamics on complex networks of various topologies (Random, Small World, Scale Free) have recently been the subject of significant amount of research and theoretical analysis, both in the Industry and Academia. See the article “Maximizing the Spread of Influence through a Social Network,” Cornell University, 2003. This research paper develops mathematical models to predict the extent to which “word-of-mouth” effects will take hold when the most “influential” individuals in the network are initially targeted but does not provide any method to quantitatively determine who those individuals are or to measure their specific degree of influence.
Also, the trend is becoming an industry unto itself and has given rise to a slew of web-based companies trying to capitalize on internet-assisted social network dynamics for a variety of business models: Support to sales teams by providing access to key customer contacts through “power networking”, job searching, or the expansion of personal relationships for dating and friendship. For example, the LinkedIn web site allows people to extend their personal relationships network by sending messages to one another but does not allow any form of one-to-many marketing application of the tool.
However, for all the requests to forward marketing messages found today in web sites and marketing campaigns, solutions for direct and systematic collection, quantification and use of individuals' word-of-mouth behavior data to improve marketing campaigns targeting and efficiency, are not available. For example, the research paper, “Mining the Network Value of Customers,” University of Washington, 2002, proposes an indirect method based on the data mining of collaborative filtering databases. Since data mining techniques consist in computing exact values on a sample population and approaching values for another population on the basis of the results on the sample population. However in this document there is no suggestion of how to compute customer Network Value based on a direct, efficient and practical observation of the sample population behavior. However, collaborative filtering does not involve direct interaction between individuals and the method detects just generic influences between customers but not the direct ones characterizing one-to-one word of mouth.
Thus, no solution is provided in the background art to detect, collect or compute Customer Network Value arising from direct observation of word-of-mouth interactions between individuals (also known as “network behavior”) for any population of customers, large or small. There is a need for an efficient method and system to determine and apply the Customer Network Value to mass marketing communication campaigns.